Performance Bounds and Sensor Placement for State Estimation Using PMUs with Phase Mismatch

被引：0

作者：

Yang, Peng ^{[1
]}

Tan, Zhao ^{[1
]}

Wiesel, Ami ^{[2
]}

Nehorai, Arye ^{[1
]}

机构：

[1] Washington Univ, Preston M Green Dept Elect & Syst Engn, St Louis, MO 63130 USA

[2] Hebrew Univ Jerusalem, Sch Comp Sci & Engn, Jerusalem 91904, Israel

来源：

2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES) | 2013年

基金：

美国国家科学基金会;

关键词：

Phasor measurement unit; state estimation; phase mismatch; posterior Cramer-Rao bound; greedy algorithm; sensor placement;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Phasor measurement units (PMU) provide time-synchronized linear measurements for power system state estimation. In practice, the synchronization is not perfect for various reasons. In this paper we derive the posterior Cramer-Rao bound on the estimation error based on a realistic measurement model which takes into account the synchronization error. We then use a greedy algorithm for PMU placement based on the derived bound, and compare the results with other heuristics and the optimal solution through exhaustive search. Numerical examples demonstrate the performance improvement using the PMU placement profile from the greedy algorithm. The results also indicate that the greedy technique closely approximates the optimal solution.

引用

页数：5

共 50 条

[31] Pursuer Aim Identification for an Aircraft Formation Using a Passive Sensor Without State Estimation [J].

Tian, Zijiao ;

Yang, Kaipei ;

Danino, Meir ;

Bar-Shalom, Yaakov ;

Milgrom, Benny .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2022, 58 (02) :1176-1186

[32] Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance [J].

Shahriar, Mohammad Shoaib ;

Habiballah, Ibrahim Omar ;

Hussein, Huthaifa .

ENERGIES, 2018, 11 (03)

[33] A Practical Multi-Phase Distribution State Estimation Solution Incorporating Smart Meter and Sensor Data [J].

Feng, Xiaoming ;

Yang, Fang ;

Peterson, William .

2012 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2012,

[34] Formulation of Three-Phase State Estimation Problem Using a Virtual Reference [J].

Langner, Andre L. ;

Abur, Ali .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2021, 36 (01) :214-223

[35] State estimation with guaranteed performance for switching-type fuzzy neural networks in presence of sensor nonlinearities [J].

Xu, Yanghui ;

Zhang, Dan .

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2014, 19 (07) :2160-2171

[36] Performance Evaluation of Hybrid State Estimation Using Real TSO Data Sources [J].

ter Vehn, Anton ;

Nordstrom, Lars ;

Apelfrojd, Senad .

18TH INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, PMAPS 2024, 2024, :335-340

[37] Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement [J].

Roy, Venkat ;

Simonetto, Andrea ;

Leus, Geert .

SENSORS, 2018, 18 (06)

[38] Detection of False Data Injection Attacks in Power System State Estimation Using Sensor Encoding [J].

Trevizan, Rodrigo D. ;

Reno, Matthew .

2022 IEEE KANSAS POWER AND ENERGY CONFERENCE (KPEC 2022), 2022,

[39] Signal processing for state estimation during sensor failure using mixed signal separation technique [J].

Ravi, S ;

Jayasingh, T .

ICCS 2002: 8TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS SYSTEMS, VOLS 1 AND 2, PROCEEDINGS, 2002, :765-768

[40] Multi-Sensor Fusion for Quadruped Robot State Estimation Using Invariant Filtering and Smoothing [J].

Nistico, Ylenia ;

Kim, Hajun ;

Soares, Joao Carlos Virgolino ;

Fink, Geoff ;

Park, Hae-Won ;

Semini, Claudio .

IEEE ROBOTICS AND AUTOMATION LETTERS, 2025, 10 (06) :6296-6303

← 1 2 3 4 5 →